Multivariate decoding and brain reading - Introduction to the special issue
NeuroImage 56 (2011) 385–386
Contents lists available at ScienceDirect
NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g
Editorial
Multivariate decoding and brain reading: Introduction to the special issue
a r t i c l e
i n f o
a b s t r a c t
In recent years, the scope of neuroimaging research has been substantially extended by multivariate decoding methodology. Decoding techniques allow us to address a number of important questions that are frequently neglected in more conventional analyses. They allow us to focus on storage of “mental content” in brain regions, rather than on overall levels of activation. They directly address the question how much information can be “read out” of brain activity patterns, thus inverting the classical direction of inference that attempts to explain brain activity from mental state variables. At the same time, they provide a much higher sensitivity to detection of effects than conventional approaches. This special issue is a showcase of research in this emerging field. Besides five invited review papers by key experts in the field, it presents a representative selection of work showing the diversity and power of multivariate decoding analyses ranging from methodological foundations to cognitive and clinical studies.
© 2011 Published by Elsevier Inc.
Introduction multivariate decoding
Traditionally, neuroimaging has been dominated by mass-univariate analyses based on the general linear model (GLM; see Friston et al., 1995). In this approach, univariate statistical tests are applied at each location of the brain individually and the statistical parameters are then plotted at each position of the brain (hence “statistical parametric mapping,” SPM). The GLM/SPM approach is highly suitable when the aim of a study is to assess whether the activity level at a single location in the brain is modulated by a specific mental operation. However, an
Contents lists available at ScienceDirect
NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g
Editorial
Multivariate decoding and brain reading: Introduction to the special issue
a r t i c l e
i n f o
a b s t r a c t
In recent years, the scope of neuroimaging research has been substantially extended by multivariate decoding methodology. Decoding techniques allow us to address a number of important questions that are frequently neglected in more conventional analyses. They allow us to focus on storage of “mental content” in brain regions, rather than on overall levels of activation. They directly address the question how much information can be “read out” of brain activity patterns, thus inverting the classical direction of inference that attempts to explain brain activity from mental state variables. At the same time, they provide a much higher sensitivity to detection of effects than conventional approaches. This special issue is a showcase of research in this emerging field. Besides five invited review papers by key experts in the field, it presents a representative selection of work showing the diversity and power of multivariate decoding analyses ranging from methodological foundations to cognitive and clinical studies.
© 2011 Published by Elsevier Inc.
Introduction multivariate decoding
Traditionally, neuroimaging has been dominated by mass-univariate analyses based on the general linear model (GLM; see Friston et al., 1995). In this approach, univariate statistical tests are applied at each location of the brain individually and the statistical parameters are then plotted at each position of the brain (hence “statistical parametric mapping,” SPM). The GLM/SPM approach is highly suitable when the aim of a study is to assess whether the activity level at a single location in the brain is modulated by a specific mental operation. However, an
References: Averbeck, B.B., Latham, P.E., Pouget, A., 2006 May. Neural correlations, population coding and computation Boynton, G.M., 2005 May. Imaging orientation selectivity: decoding conscious perception in V1 Epstein, R., Kanwisher, N., 1998 Apr 9. A cortical representation of the local visual environment Friston, K.J., Holmes, A.P., Poline, J.B., Grasby, P.J., Williams, S.C., Frackowiak, R.S., Turner, R., 1995 Mar Georgopoulos, A.P., Schwartz, A.B., Kettner, R.E., 1986 Sep 26. Neuronal population coding of movement direction Haynes, J.D., Rees, G., 2006 Jul. Decoding mental states from brain activity in humans. Haxby, J.V., Gobbini, M.I., Furey, M.L., Ishai, A., Schouten, J.L., Pietrini, P., 2001 Sep 28. Kamitani, Y., Tong, F., 2005 May. Decoding the visual and subjective contents of the human brain Kanwisher, N., McDermott, J., Chun, M.M., 1997 Jun 1. The fusiform face area: a module in human extrastriate cortex specialized for face perception Kriegeskorte, N., Cusack, R., Bandettini, P., 2010 Feb 1. How does an fMRI voxel sample the neuronal activity pattern: compact-kernel or complex spatiotemporal filter? Neuroimage. 49 (3), 1965–1976 Epub 2009 Oct 1. Moutoussis, K., Zeki, S., 2002 Jul 9. The relationship between cortical activation and perception investigated with invisible stimuli Op de Beeck, H.P., 2010 Feb 1. Against hyperacuity in brain reading: spatial smoothing does not hurt multivariate fMRI analyses Swisher, J.D., Gatenby, J.C., Gore, J.C., Wolfe, B.A., Moon, C.H., Kim, S.G., Tong, F., 2010 Jan 6 Tanaka, K., 1996. Inferotemporal cortex and object vision. Annu. Rev. Neurosci. 19, 109–139. Tong, F., Nakayama, K., Vaughan, J.T., Kanwisher, N., 1998 Oct. Binocular rivalry and visual awareness in human extrastriate cortex Tononi, G., Srinivasan, R., Russell, D.P., Edelman, G.M., 1998 Mar 17. Investigating neural correlates of conscious perception by frequency-tagged neuromagnetic responses. Proc. Natl. Acad. Sci. USA 95 (6), 3198–3203. Bernstein Center for Computational Neuroscience, Haus 6, Philippstrasse 13, 10115 Berlin, Germany